Staff Machine Learning Engineer
Coupang · Taipei
Company Introduction:
Coupang is reimagining the shopping experience with the goal of wowing each customer from the instant they open the Coupang app to the moment an order is delivered to their door.
Our services in Taiwan include “Rocket Delivery” which offers next-day delivery for a wide selection of items at affordable prices, “Rocket Oversea” which offers free international delivery on millions of best-selling products from Korea, the U.S., and beyond.
We are looking for talents to help us lead Coupang’s expansion in Taiwan. This is an exceptional opportunity to become a part of Coupang’s growth in Taiwan and create a world where our customers wonder, “How did I ever live without Coupang?”
Role Overview:
This role is ideal for a pragmatic and impact-driven builder who is passionate about bridging the gap between data science and production systems. You will collaborate closely with Data Scientists to bring predictive models to life, establishing robust deployment pipelines, setting up foundational infrastructure, and driving MLOps best practices. The successful candidate will thrive in laying down technical foundations from scratch, ensuring scalability, maintainability, and high performance for our data and data science workflows.
What You Will Do:
- Collaborate closely with Data Scientists and Data Analysts to translate experimental code into production-ready architectures, driving code refactoring, model hosting decisions, and seamless integration.
- Design and formalize end-to-end model deployment processes, establishing standardized workflows for model versioning management to ensure reproducibility and reliability in production.
- Build and maintain robust data pipelines leveraging modern big data and distributed processing ecosystems, while establishing standardized Data Science development environments to boost cross-functional team productivity.
- Implement foundational ML infrastructure and standards, including feature store integration and data versioning management, tailored to support scalable business growth.
- Develop and operationalize predictive solutions using appropriate statistical, analytical, or heuristic approaches, when business needs require, bridging analytical insights with operational execution.
- Monitor, maintain, and optimize existing deployed solutions, proactively identifying bottlenecks, addressing technical debt, and ensuring high availability of production models.
Basic Qualifications
- Bachelor's/Master's degree in computer science, software engineering, industrial engineering, or any other quantitative disciplines, with 5+ years of relevant software/ML engineering experience in industry
- Hands-on experience in MLOps and model deployment lifecycle, including code refactoring, containerization (Docker, Kubernetes), and model hosting strategies (e.g., FastAPI, REST APIs, or cloud-native endpoints)
- Solid proficiency in Python, SQL, and distributed data processing frameworks (e.g., Spark, Hive, or equivalent big data technologies), with proven experience in building and maintaining scalable data pipelines
- Practical experience in setting up and standardizing ML foundations, such as feature stores, data versioning tools (e.g., DVC), and model registry/versioning management systems
- Strong software engineering best practices, including version control (Git), CI/CD pipelines, code testing, and clean architecture design
- Excellent written and verbal communication skills to effectively collaborate with Data Scientists, product managers, and software engineering stakeholders
Preferred Qualifications:
- Demonstrated experience working side-by-side with Data Scientists in an applied research or advanced analytics environment
- Familiarity with building and deploying lightweight or moderate-complexity machine learning models (e.g., forecasting, classification, or regression tasks)
- Experience evaluating and implementing internal tooling and lightweight frameworks to streamline DS workflows and accelerate time-to-market
- A deep understanding of the e-commerce or supply chain domain, with a track record of driving scalable engineering solutions that deliver measurable business impact
Recruitment Process
- Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer
- The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances.
- Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage.
Details to Consider
- This job posting may be closed prior to the stated end date for application if all openings are filled.
- Coupang has the right to rescind an offer of employment if a candidate is found to have
submitted false information as part of the application process.
- Coupang does not discriminate against disabled applicants or those with veteran status.
- We are proud to offer equal opportunities for all applicants.